A face liveness detection technology is used to determine whether the current user is the authentic user by using facial recognition techniques so as to intercept spoofing attacks such as a screen replay attack, a printed photo attack, and a three-dimensional modeling attack.
Currently, the face liveness detection technology can be classified into an intrusive face liveness detection technology and a non-intrusive face liveness detection technology. In the intrusive face liveness detection technology, a user needs to cooperatively complete some specific live actions such as blinking, head turning, or mouth opening. When performing facial recognition based on the given instructions, the liveness detection module can determine whether an operator accurately completes the live operation and whether the operator is the authentic user. In the non-intrusive face liveness detection technology, a user does not need to cooperatively complete a live action, so that user experience is better, but the technical complexity is higher. In addition, liveness detection is performed mainly depending on information about an input single frame image or information about other device sensors.
In the described non-intrusive face liveness detection technology in the existing technology, supervised training is usually performed on a single deep learning model by using live and non-live facial images, and then face liveness prediction is performed on the input single frame image by using the trained model.
However, such a technical solution heavily depends on a spoofing face attack type of training data, and is limited by an objective condition of insufficient training data. It is difficult to fully extract a live face image feature. As a result, this model cannot fully express a live face feature, and accuracy of a face liveness detection result is reduced.